--- base_model: akhooli/sbert_ar_nli_500k_norm library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: الطريقة الأفضل لتحصل على ريتويت ولايك هل ايام تقتطع جزء من مقابلة للوزير باسيل طبعا جزء غير مكتمل ثم تغرد به وتقول جبران مع التطبيع - text: ما بعرف كيف بدي خبركن ياها بس غير إنو واطي طلع عرص - text: سد نيعك يا صرمايت بشار - text: بتفهّم فهم وحدة فهما متدنّي هههه 😎 - text: للاسف لدينا في الخليج بعض من الكتاب والدكاتره والمحللين كالبغال والجحاش ينهق ويهرف بما لايعرف inference: true model-index: - name: SetFit with akhooli/sbert_ar_nli_500k_norm results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.8544600938967136 name: Accuracy --- # SetFit with akhooli/sbert_ar_nli_500k_norm This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert_ar_nli_500k_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_norm) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [akhooli/sbert_ar_nli_500k_norm](https://huggingface.co/akhooli/sbert_ar_nli_500k_norm) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 2 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | negative | | | positive | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.8545 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("akhooli/setfit_ar_hs") # Run inference preds = model("سد نيعك يا صرمايت بشار") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 12.2912 | 52 | | Label | Training Sample Count | |:---------|:----------------------| | negative | 2015 | | positive | 2800 | ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (1, 1) - max_steps: 8000 - sampling_strategy: undersampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - run_name: setfit_hate_25kv8 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0003 | 1 | 0.3359 | - | | 0.025 | 100 | 0.2843 | - | | 0.05 | 200 | 0.2376 | - | | 0.075 | 300 | 0.2067 | - | | 0.1 | 400 | 0.1591 | - | | 0.125 | 500 | 0.108 | - | | 0.15 | 600 | 0.0736 | - | | 0.175 | 700 | 0.0513 | - | | 0.2 | 800 | 0.0384 | - | | 0.225 | 900 | 0.0364 | - | | 0.25 | 1000 | 0.0296 | - | | 0.275 | 1100 | 0.0207 | - | | 0.3 | 1200 | 0.0212 | - | | 0.325 | 1300 | 0.0164 | - | | 0.35 | 1400 | 0.0122 | - | | 0.375 | 1500 | 0.0163 | - | | 0.4 | 1600 | 0.01 | - | | 0.425 | 1700 | 0.0085 | - | | 0.45 | 1800 | 0.0081 | - | | 0.475 | 1900 | 0.0083 | - | | 0.5 | 2000 | 0.0057 | - | | 0.525 | 2100 | 0.0061 | - | | 0.55 | 2200 | 0.0046 | - | | 0.575 | 2300 | 0.0049 | - | | 0.6 | 2400 | 0.007 | - | | 0.625 | 2500 | 0.0048 | - | | 0.65 | 2600 | 0.0057 | - | | 0.675 | 2700 | 0.0058 | - | | 0.7 | 2800 | 0.0046 | - | | 0.725 | 2900 | 0.0044 | - | | 0.75 | 3000 | 0.0042 | - | | 0.775 | 3100 | 0.0042 | - | | 0.8 | 3200 | 0.0057 | - | | 0.825 | 3300 | 0.003 | - | | 0.85 | 3400 | 0.0041 | - | | 0.875 | 3500 | 0.0052 | - | | 0.9 | 3600 | 0.004 | - | | 0.925 | 3700 | 0.0042 | - | | 0.95 | 3800 | 0.0058 | - | | 0.975 | 3900 | 0.0049 | - | | 1.0 | 4000 | 0.0052 | - | | 1.025 | 4100 | 0.0031 | - | | 1.05 | 4200 | 0.0025 | - | | 1.075 | 4300 | 0.003 | - | | 1.1 | 4400 | 0.0018 | - | | 1.125 | 4500 | 0.0015 | - | | 1.15 | 4600 | 0.0038 | - | | 1.175 | 4700 | 0.0033 | - | | 1.2 | 4800 | 0.0031 | - | | 1.225 | 4900 | 0.0022 | - | | 1.25 | 5000 | 0.0023 | - | | 1.275 | 5100 | 0.0022 | - | | 1.3 | 5200 | 0.0027 | - | | 1.325 | 5300 | 0.0017 | - | | 1.35 | 5400 | 0.0027 | - | | 1.375 | 5500 | 0.0019 | - | | 1.4 | 5600 | 0.0024 | - | | 1.425 | 5700 | 0.0015 | - | | 1.45 | 5800 | 0.0023 | - | | 1.475 | 5900 | 0.0021 | - | | 1.5 | 6000 | 0.0009 | - | | 1.525 | 6100 | 0.0015 | - | | 1.55 | 6200 | 0.0009 | - | | 1.575 | 6300 | 0.001 | - | | 1.6 | 6400 | 0.0002 | - | | 1.625 | 6500 | 0.0004 | - | | 1.65 | 6600 | 0.0012 | - | | 1.675 | 6700 | 0.0011 | - | | 1.7 | 6800 | 0.0008 | - | | 1.725 | 6900 | 0.0013 | - | | 1.75 | 7000 | 0.0004 | - | | 1.775 | 7100 | 0.0004 | - | | 1.8 | 7200 | 0.0008 | - | | 1.825 | 7300 | 0.0007 | - | | 1.85 | 7400 | 0.0007 | - | | 1.875 | 7500 | 0.001 | - | | 1.9 | 7600 | 0.001 | - | | 1.925 | 7700 | 0.0002 | - | | 1.95 | 7800 | 0.0005 | - | | 1.975 | 7900 | 0.0009 | - | | 2.0 | 8000 | 0.0002 | - | ### Framework Versions - Python: 3.10.14 - SetFit: 1.2.0.dev0 - Sentence Transformers: 3.2.1 - Transformers: 4.45.1 - PyTorch: 2.4.0 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```